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Scientific Publication Citing Dragonfly

Optimization of FIB–SEM tomography and reconstruction for soft, porous, and poorly conducting materials

Cecilia Fager (1), Magnus Röding (2), Anna Olsson (3), Niklas Lorén (1,2), Christian von Corswant (3), Aila Särkkä (4), Eva Olsson (1)
Microscopy and Microanalysis, May 2020: 1-9. DOI: 10.1017/S1431927620001592

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Keywords

3D, focused ion beam, poorly conducting material, scanning electron microscopy, soft material, tomography

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Abstract

Tomography using a focused ion beam (FIB) combined with a scanning electron microscope (SEM) is well-established for a wide range of conducting materials. However, performing FIB–SEM tomography on ion- and electron-beam-sensitive materials as well as poorly conducting soft materials remains challenging. Some common challenges include cross-sectioning artifacts, shadowing effects, and charging. Fully dense materials provide a planar cross section, whereas pores also expose subsurface areas of the planar cross-section surface. The image intensity of the subsurface areas gives rise to overlap between the grayscale intensity levels of the solid and pore areas, which complicates image processing and segmentation for three-dimensional (3D) reconstruction. To avoid the introduction of artifacts, the goal is to examine porous and poorly conducting soft materials as close as possible to their original state. This work presents a protocol for the optimization of FIB–SEM tomography parameters for porous and poorly conducting soft materials. The protocol reduces cross-sectioning artifacts, charging, and eliminates shadowing effects. In addition, it handles the subsurface and grayscale intensity overlap problems in image segmentation. The protocol was evaluated on porous polymer films which have both poor conductivity and pores. 3D reconstructions, with automated data segmentation, from three films with different porosities were successfully obtained.

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How Our Software Was Used

Dragonfly was used to perform the 3D reconstruction of materials from binary 2D image stacks.

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Author Affiliation

(1) Department of Physics, Chalmers University of Technology, Gothenburg SE-41296, Sweden.
(2) RISE Research Institutes of Sweden, Agrifood and Bioscience, Gothenburg, Sweden.
(3) AstraZeneca R&D Mölndal, Mölndal SE43183, Sweden.
(4) Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.

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